Sample metadata

A total of 19 metadata variables were imported from the sample sheet for this sample: -

Number of counts / features per cellular barcode

Figure: Histogram of count depth per cell. A lower-limit threshold of 300 was applied (red line).

Figure: Histogram of number of genes per cell. A lower-limit threshold of 100 was applied (red line).

Count depth distribution by barcode rank (high to low counts)

<b>Figure: Barcode count depth rank plot.</b> The 'elbow' indicates where count depth decreases rapidly (relative increase in background counts), and can be used to inform the count depth threshold. The applied lower-limit counts threshold is indicated at 300 counts (red line).

Figure: Barcode count depth rank plot. The ‘elbow’ indicates where count depth decreases rapidly (relative increase in background counts), and can be used to inform the count depth threshold. The applied lower-limit counts threshold is indicated at 300 counts (red line).

Number of genes versus count depth

<b>Figure: Number of genes versus count depth coloured by relative mitochondrial counts.</b> The count-depth threshold of 300 counts and the number of genes threshold of 100 genes are indicated with vertical and horizontal red lines, respectively. Cells with high mitochondrial counts are typically in cells with relatively lower count depth. Cells with fractional mitochondrial counts higher than 0.1 (i.e. 10.00%) were filtered.

Figure: Number of genes versus count depth coloured by relative mitochondrial counts. The count-depth threshold of 300 counts and the number of genes threshold of 100 genes are indicated with vertical and horizontal red lines, respectively. Cells with high mitochondrial counts are typically in cells with relatively lower count depth. Cells with fractional mitochondrial counts higher than 0.1 (i.e. 10.00%) were filtered.

Fraction of mitochondrial / ribosomal counts

Figure: Histogram of mitochondrial fraction per cell. A upper-threshold of 0.1 (i.e. 10.00%) maximum mitochondrial fraction was applied (red line).

Figure: Histogram of ribosomal fraction per cell. A upper-threshold of 1 (i.e. 100.00%) maximum ribosomal fraction was applied (red line).

Doublet/multiplet identification

A total of 5106 cells which passed QC were submitted to the multiplet identification algorithm “doubletfinder”. This identified 209 multiplets and 4897 singlets. To identify these, the variable(s) "" were first regressed out of the data. The first 10 principal components were used to identify the 2000 most variable genes. An assumed doublet formation rate of 0.040848 (i.e. 4.08% ) was applied.

Parameter sweep for optimal pK

A pK value of 0.005 was specified, therefore a parameter sweep was not performed.

Multiplet visualization in two-dimensional space

The 209 multiplets identified by “doubletfinder” are visualized below in red in PCA space, tSNE space, and UMAP space.

<b>PCA</b>

PCA

<b>tSNE</b>

tSNE

<b>UMAP</b>

UMAP

Full QC parameters and results

References

McGinnis, Christopher S., Murrow, Lyndsay M., Gartner, Zev J. (2019). DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Systems. 8(4), 329–337.e4. [DOI]

Stuart, Tim, Butler, Andrew, Hoffman, Paul, Hafemeister, Christoph, Papalexi, Efthymia, Mauck, William M., Hao, Yuhan, Stoeckius, Marlon, Smibert, Peter, Satija, Rahul (2019). Comprehensive Integration of Single-Cell Data. Cell. 177(7), 1888–1902.e21. [DOI]


scflow v0.3.0 – 2019-12-05 15:53:13

 

A report by scflow